Computer-aided grading and quantification of hip osteoarthritis severity employing shape descriptors of radiographic hip joint space
Computer-aided grading and quantification of hip osteoarthritis severity employing shape descriptors of radiographic hip joint space
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DOI：
10.1016/j.compbiomed.2007.05.005
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发表时间：
2007-12-01
影响因子：
7.7
通讯作者：
Panayiotakis, George
中科院分区：
文献类型：
--
作者：
Boniatis, Loannis;Cavouras, Dionisis;Panayiotakis, George
设计了一个基于计算机的系统，用于髋关节骨关节炎（OA）严重程度的分级和量化。采用主动轮廓分割模型，从32例单侧和双侧OA患者的数字化X线片中获得64个髋关节间隙（HJS）图像（18个正常，46个骨关节炎）。从HJS图像生成的形状特征和层次决策树结构用于OA的分级。基于形状特征的回归模型量化OA严重程度。该系统在将髋关节表征为“正常”（100%）、“轻度/中度”-OA（93.8%）或“重度”-OA（96.7%）方面实现了高准确性。由HJS狭窄表示的OA严重程度值与回归模型预测的值高度相关（r = 0.9，p < 0.001）。该系统可以有助于OA患者管理。(c)2007爱思唯尔有限公司保留所有权利。
A computer-based system was designed for the grading and quantification of hip osteoarthritis (OA) severity. Employing an active-contours segmentation model, 64 hip joint space (HJS) images (18 normal, 46 osteoarthritic) were obtained from the digitized radiographs of 32 unilateral and bilateral OA-patients. Shape features, generated from the HJS-images, and a hierarchical decision tree structure was used for the grading of OA. A shape features based regression model quantified the OA-severity. The system accomplished high accuracies in characterizing hips as "Normal" (100%), of "mild/moderate"-OA (93.8%) or "severe"-OA (96.7%). OA-severity values, as expressed by HJS-narrowing, correlated highly (r = 0.9, p < 0.001) with the values predicted by the regression model. The system may contribute to OA-patient management. (c) 2007 Elsevier Ltd. All rights reserved.